1. Elucidating effects of nerve injury on gene expression using.
Bio-Ontologies SIG – July
Alison Callahan, Matthew C. Danzi, Giulia Zunino, Daniel J. Cooper, Nigam H. Shah, Ubbo Visser, John L. Bixby, and Vance P. Lemmon

2. Spinal cord injury is a significant burden on individuals and the U. S
Spinal cord injury is a significant burden on individuals and the U.S. healthcare system
~12,500 new SCI cases each year in the U.S.
~276,000 total individuals affected in 2014
average yearly expenses in the first year after injury range from $350K to >$1M depending on severity

3. There are no effective therapies for spinal cord injury – is this because experiments are not replicable?
Steward et al Replication and reproducibility in spinal cord injury research. Experimental Neurology.
Prinz et al Believe it or not: how much can we rely on published data on potential drug targets? Nature Drug Discovery.
Mechanisms of injury image: Ruff and Fehlings Neural stem cells in regenerative medicine: bridging the gap. Panminerva medica 52(2):

4. Aggregating and linking data across studies and experiment types is managed by individual scientists
Sejnowski et al Putting big data to good use in neuroscience. Nature Neuroscience.

5. We have lots of data at our disposal

6. Goal: Structure and integrate SCI research relevant data

7. The RegenBase ontology
defines classes and properties specific to the SCI research domain
435 classes
18 object properties
8 data properties
mappings to FMA and MPO based on lexical match of class labels

8. Getting information from the literature into RegenBase
SCI + regeneration related publications
Expert curation
+ RDF conversion pipeline
+ entity identifier mapping

9. MIASCI Online A tool for SCI researchers to curate publications
11 major sections: investigator, organism, surgery, perturbagen, cell transplantation, biomaterials, histology, immunohistochemistry, imaging, behavior, and data analysis and statistics.

10. Literature-sourced data model

11. Literature-sourced data model

12. Getting assay data into RegenBase
Raw assay data
Kinase activity assays
Neurite outgrowth assays
Data processing
+ RDF conversion
+ entity identifier mapping

13. Assay data model

14. Getting gene expression data into RegenBase
Raw RNA-seq or microarray data
Data processing
+ RDF conversion
+ entity identifier mapping

15. Gene expression data model

16. RegenBase content
literature-sourced data: ~20,000 statements from 42 publications
kinase activity data: effect of ~52,000 compounds on 476 kinases
neurite outgrowth data: effect of ~1600 compounds on neurite outgrowth
gene expression data: changes in gene expression after injury in rats and mice for > 40,000 genes and gene probes
Callahan et al RegenBase: a knowledge base of spinal cord injury biology for translational research. Database (Oxford) 16: baw040.

17. Gene expression data model is motivated by 3 use cases
Image credits:
- Protein - Thomas Splettstoesser (
- Sequence homology - Thomas Shafee
- Gene expression -

18. Use case #1: What genes significantly differentially expressed in DRGs after a peripheral nerve injury have a protein product with an RNA-recognition motif?
Symbol
Fold change
P-value
Time (hours)
A1cf 1 3 24 28
Acin1 72
Cirbp 8 12
Cpsf6
Cpsf7

19. Use case #2: Does the mouse gene CALM2 have any rat gene orthologues that are significantly differentially expressed in DRG neurons after a peripheral nerve injury?

20. Use case #3: What genes are differentially regulated at the early time points after injury, but then move toward to their homeostatic levels (or even go the opposite direction) later?
Gene Symbol
1st Fold Change
1st P-value
2nd Fold Change
2nd P-value
Pdpk1
5.59
4.56E-10
4.39
7.57E-08
Cdh22
5.26
2.85E-06
-0.056
0.999
Tcf4
4.52
9.24E-05
-0.088
Flrt3
4.51
1.07E-13
2.86
3.17E-08
Il6
4.42
1.42E-13
3.08
4.25E-09
Cacna2d1
4.35
1.73E-08
3.80
2.14E-07
Kcna1
4.33
Ap3s1
4.32
2.02E-09
4.13
3.70E-09
Gnb1
4.05
3.56
Gda
3.98
3.85

22. RegenBase and the Linked Data Web enable faster, easier SCI data search and analysis
We have extended RegenBase with an important new data source, and the code we developed to do this is re-usable
Each of the 3 research use cases require many researcher hours if executed “by hand”, each time a gene of interest is identified
RegenBase reduces this time to minutes for query formulation and seconds for query response
Using URI patterns, identifier mapping services, and Bio2RDF gives us data integration for free

23. What next?
A RegenBase search tool, new methods and data sources for adding content to RegenBase, and working with the broader neuroscience community to extend to new domains

24. Acknowledgements Literature curators RegenBase team
John Bixby
Vance Lemmon
Ubbo Visser
Shah Stanford
funding: NLM R01s HD and NS080145

25. Thank you! questions? more information available online
- project description, simple paper browser, example queries, data download
- RegenBase ontology in BioPortal
- SPARQL endpoint